Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks

Tahisa Neitzel Kuck,Ricardo Dalagnol,Paulo Fernando Ferreira Silva Filho,Elcio Hideiti Shiguemori,Polyanna Da Conceição Bispo,Edson Eyji Sano

doi:10.3390/rs13234944

Abstract

It is estimated that, in the Brazilian Amazon, forest degradation contributes three times more than deforestation for the loss of gross above-ground biomass. Degradation, in particular those caused by selective logging, result in features whose detection is a challenge to remote sensing, due to its size, space configuration, and geographical distribution. From the available remote sensing technologies, SAR data allow monitoring even during adverse atmospheric conditions. The aim of this study was to test different pre-trained models of Convolutional Neural Networks (CNNs) for change detection associated with forest degradation in bitemporal products obtained from a pair of SAR COSMO-SkyMed images acquired before and after logging in the Jamari National Forest. This area contains areas of legal and illegal logging, and to test the influence of the speckle effect on the result of this classification by applying the classification methodology on previously filtered and unfiltered images, comparing the results. A method of cluster detections was also presented, based on density-based spatial clustering of applications with noise (DBSCAN), which would make it possible, for example, to guide inspection actions and allow the calculation of the intensity of exploitation (IEX). Although the differences between the tested models were in the order of less than 5%, the tests on the RGB composition (where R = coefficient of variation; G = minimum values; and B = gradient) presented a slightly better performance compared to the others in terms of the number of correct classifications for selective logging, in particular using the model Painters (accuracy = 92%) even in the generalization tests, which presented an overall accuracy of 87%, and in the test on RGB from the unfiltered image pair (accuracy of 90%). These results indicate that multitemporal X-band SAR data have the potential for monitoring selective logging in tropical forests, especially in combination with CNN techniques.

Highlights

Land use, land use changes, and forests have historically been the sectors that most contribute to greenhouse gas emissions in Brazil, according to the Greenhouse Gas Emissions and Removal Estimates System—SEEG [1]
The study area is located in the Jamari National Forest (NF), an area covered by native tropical forest, protected by the Brazilian State
The classification tests by pre-trained Convolutional Neural Networks (CNNs) on the covmingrad, images are presented in Table 1A, on the cov image in Table 1B, and on the ratio image in Table 1C, with all images derived from the filtered COSMO Sky-Med image pair

Summary

Introduction

Land use changes, and forests have historically been the sectors that most contribute to greenhouse gas emissions in Brazil, according to the Greenhouse Gas Emissions and Removal Estimates System—SEEG [1]. 2021, 13, 4944 and a near-real time system of deforestation and degradation alerts, aimed at monitoring actions, called DETER-B [3]. In addition to these governmental systems, scientists and non-governmental organizations have proposed new operational methods aimed at detecting, mapping, and monitoring deforestation in tropical regions through different techniques and sensors [4,5,6,7]. Despite being widely explored for deforestation mapping, the application of remote sensing to monitor forest degradation still requires advances, especially due to the complexity of the nature of these processes [8].

Objectives

Methods

Discussion

Conclusion